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Financial forecasting plays an important role in making informed decisionsfor financial stakeholders, specifically in the stock exchange market. In atraditional setting, investors commonly rely on the equity research departmentfor valuable reports on market insights and investment recommendations. Theequity research department, however, faces challenges in effectuatingdecision-making do to the demanding cognitive effort required for analyzing theinherently volatile nature of market dynamics. Furthermore, financialforecasting systems employed by analysts pose potential risks in terms ofinterpretability and gaining the trust of all stakeholders. This paper presentsan interpretable decision-making model leveraging the SHAP-based explainabilitytechnique to forecast investment recommendations. The proposed solution notonly provides valuable insights into the factors that influence forecastedrecommendations but also caters the investors of varying types, including thoseinterested in daily and short-term investment opportunities. To ascertain theefficacy of the proposed model, a case study is devised that demonstrates anotable enhancement in investor's portfolio value, employing our tradingstrategies. The results highlight the significance of incorporatinginterpretability in forecasting models to boost stakeholders' confidence andfoster transparency in the stock exchange domain.

Enhancing Profitability and Investor Confidence through Interpretable AI Models for Investment Decisions